AI Startups' First 90 Days: Product-Market Fit, Distribution, and Scaling to $10M ARR

A panel of founders and investors discusses what early-stage founders should prioritize in their first 90 days in the AI era. The conversation covers building products, finding product-market fit, distribution strategies, sustainable revenue growth, and the metrics that matter on the path to $10M ARR.

■ Early-stage expectations and AI-era building
- How AI tools have made product building easier while making differentiation harder
- Founder-led growth, launch tactics, and the role of San Francisco
- Using AI agents for fundraising, outreach, and early customer acquisition

■ Go-to-market and distribution
- Virality versus retention and the shift from product-led to enterprise sales
- Automating outreach, ads, and content creation with agents
- Building repeatable go-to-market motions after initial traction

■ Metrics, moats, and scaling
- ARR quality, CAC/LTV, net dollar retention, and inference resale margins
- Changes in sales quotas and compensation in AI-driven businesses
- Proactive versus ex post facto moats and application-layer competition

■ Customer retention and product signals
- Identifying real pain points, workflow adoption, and habit formation
- When to start the 90-day clock and common founder mistakes
- Community building and staying close to customers

This panel is suited for founders, operators, and investors interested in early-stage AI go-to-market and scaling. Viewers can expect frameworks for evaluating product-market fit, distribution, retention, and revenue metrics, along with practical questions to ask before scaling an AI startup.